Contents 
import pandas as pd
import json

# charts and images
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import seaborn as sns
from PIL import Image as pilimage
import base64, io
from IPython.display import Markdown as md

Measuring changes in litter density before and after restoration

In 2015 we began a project with a goal to reduce and prevent litter in the Lower American River,and one of its tributaries, Cordova Creek. One of the project objectives was to quantify the debris found there by performing weekly surveys. Regular reports were posted. After nine months, an average density of debris items for the project, based upon the daily total for each day surveyed, was 1.98 debris objects per foot(or 6.496 per meter), of creek bank. Surveys continued for another nine months in order to establish a baseline for future referrence. This repository provides background on our first project, its survey results, and the current project results.

Background on cordova creek

The Cordova Creek Naturalization Project(started in early 2016), restored natural form and function to a portion of the channelized stream previously named the Clifton Drain. The naturalized waterway, which provides both critical habitat and water quality benefits, was renamed Cordova Creek.

History of Naturalization Project

The River Bend Park Area Plan calls for restoration of Cordova Creek to create a riparian corridor that will filter urban runoff and serve as a buffer between nature study area to the west and limited and developed recreational uses to the east. It also calls for bringing together native plant nursery, organic farming operation and riparian habitat to enhance interpretation opportunities.

Methods

Removal Survey Protocols Listed below, are the sequence of steps required for safe, efficient removal and documentation of the pollutant debris.

  1. Take pictures of location, including visible debris present

  2. Note location, date, length of survey area

  3. Remove all visible debris items from within the survey perimeter using standard retrieval methods/equipment

  4. Place all debris items on a tarp

  5. Count and photograph the array of items

  6. Enter data in the survey app

  7. Once all items have been documented dispose, or recycle

“)

output = io.BytesIO()

this_image = pilimage.open("images/demotarp.jpg")
this_image.thumbnail((1600, 900))
this_image.save(output, format='PNG')
encoded_string = base64.b64encode(output.getvalue()).decode()

html = '<img src="data:image/png;base64,{}"/>'.format(encoded_string)
md(html)

#hd personnel documenting trash objects using survey form.

output = io.BytesIO()

this_image = pilimage.open("images/100_5208.JPG")
this_image.thumbnail((1600, 900))
this_image.save(output, format='PNG')
encoded_string = base64.b64encode(output.getvalue()).decode()

html = '<img src="data:image/png;base64,{}"/>'.format(encoded_string)
md(html)

#The drainage ditch known as Clifton's Drain before restoration. 

# using the server data
server_data = pd.read_csv('resources/the_data.csv')

# drop unnamed column:
def drop_this_column(data, column='Unnamed: 0'):
    if column in data.columns:
        data.drop(column, inplace=True, axis=1)
for datas in [server_data]:
    drop_this_column(datas)

fd = server_data[server_data.location == 'cordova-creek'].copy()

# number of samples
fd.loc_date.nunique()

56

# total collected
fd.quantity.sum()

9212

# median pieces of trash per meter of shoreline (foot?)
x = fd.groupby('loc_date').pcs_m.sum()
x.median()

5.26

# the top 20 codes
codes = fd.groupby('code', as_index=False).agg({'pcs_m':'median', 'quantity':'sum'})
codes.sort_values(by='quantity', ascending=False)[:20]
code pcs_m quantity
61 G82 0.56 1488
42 G30 0.59 1245
39 G27 0.30 861
58 G79 0.30 537
15 G156 0.15 334
41 G3 0.15 312
37 G25 0.17 262
32 G21 0.11 225
13 G153 0.11 218
56 G7 0.11 217
47 G35 0.11 202
45 G33 0.11 196
23 G178 0.11 194
36 G24 0.15 188
22 G177 0.11 170
35 G23 0.11 144
44 G32 0.07 132
0 G10 0.07 130
29 G200 0.07 120
3 G124 0.11 115 
# time series


dt = fd.groupby(['loc_date', 'date'], as_index=False).pcs_m.sum()
dt['date'] = pd.to_datetime(dt['date'], format="%Y-%m-%d")

fig, ax = plt.subplots(figsize=(6,5))

sns.scatterplot(data=dt, x='date', y='pcs_m', ax=ax, s=60)
mnrs = mdates.WeekdayLocator(byweekday=1, interval=1, tz=None)
mjrs = mdates.MonthLocator(interval=1, tz=None)
ax.xaxis.set_minor_locator(mnrs)
ax.xaxis.set_major_locator(mjrs)
ax.xaxis.set_major_formatter(mdates.DateFormatter('%m'))
_images/year_1_14_0.png

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Survey_2022